The increasing frequency of natural disasters has heightened the demand for UAV (Unmanned Aerial Vehicle) technologies. UAVs, especially drones, can monitor remote disaster areas and provide situational awareness to emergency responders. Equipped with cameras and onboard computers, drones can detect survivors in real-time, enhancing the efficiency of Search and Rescue (SAR) operations. Due to limited battery capacity, the drones must be deployed along a path of the shortest possible length to avoid delays in detecting the survivors in a given disaster area. Traditional path-planning algorithms struggle to address the dynamic conditions in disaster areas. We propose an adaptive drone path planning framework for real-time survivor detection in disaster areas to address this. This framework aims to improve survivor detection by guiding UAVs along routes with higher probabilities of the presence of survivors. Adopting a ”Learn-As-You-Go” strategy, it trains a Potential Survivor Location (PSL) prediction model to identify way-points for drone sorties. Next, it leverages a novel computationally efficient path planning approach called Prediction-Based Priority-Aware Path Planning (PB-PAPP) to navigate towards the identified PSLs. Also, we present a Weight Synthesis module that enhances the prediction quality over time by aggregating the weights of the models trained by the drones, allowing continuous adaptation in changing environments. Finally, we present a prototype lightweight decentralized machine learning system that combines the above modules to facilitate real-time survivor detection. Compared to existing algorithms, our framework demonstrates an 84-97% reduction in overhead for adaptive path-planning.